Blocked sensor detection and notification

ABSTRACT

Systems and techniques are provided for blocked sensor detection and notification. A signal may be received from a sensor. The sensor may be determined to be producing anomalous output based on checking the signal from the sensor against a signal from a second sensor on the same sensor device as the sensor, checking the signal from the sensor against signals from sensors on additional sensor devices, and checking the signal from the sensor against a slow temporal model for the sensor. In response to the determination that the sensor is producing anomalous output, a notification may be generated indicating the sensor is producing anomalous output, and a confidence level in the sensor may be degraded.

BACKGROUND

A smart home environment may include sensors that monitor variousaspects of an environment such as a home. Motion sensors may monitorrooms in the home for motion, and may be able to generate an alert whenmotion is detected in a room in which no motion is expected. Othersensors, such as cameras, microphones, and light sensors may also beable to generate alarms when they detect anomalous occurrences, such asthe voices or lights being turned on in a room, which is supposed to beempty.

Some sensors of the smart home environment may become blocked. Objects,such as purses, bags, or boxes, may be placed in front of sensors,limiting the sensors ability to monitor the environment. For example, apurse placed in front of a motion sensor may prevent the motion sensorfrom detecting motion in a room. A blocked sensor may not be triggeredwhen activity that would otherwise trigger the sensor occurs, and maynot generate alerts when necessary. For example, a blocked motion sensormay not generate an alert when there is motion in the room monitored bythe sensor even when there should not be anybody moving in the room.

BRIEF SUMMARY

According to an embodiment of the disclosed subject matter, a signal maybe received from a sensor. The sensor may be determined to be producinganomalous output based on checking the signal from the sensor against asignal from a second sensor on the same sensor device as the sensor,checking the signal from the sensor against signals from sensors onadditional sensor devices, and checking the signal from the sensoragainst a slow temporal model for the sensor. In response to thedetermination that the sensor is producing anomalous output, anotification may be generated indicating the sensor is producinganomalous output, and a confidence level in the sensor may be degraded.

The signal from the sensor may be checked against a signal from a secondsensor on the same sensor device as the sensor further comprises. Thesignal from the second sensor on the same sensor device as the sensormay be received. It may be determined that the signal from the sensor isinconsistent with the signal from the second sensor. An indication thatthe sensor is producing anomalous output may be generated.

The signal from the sensor may be checked against signals from sensorson additional sensor devices. The signals from the sensors on theadditional sensor devices may be received. It may be determined that thesignal from the sensor is inconsistent with one of the signals from thesensors. It may be determined that the signals from the sensors areconsistent with each other. An indication that the sensor is producinganomalous output may be generated.

The signal from the sensor may be checked against a slow temporal modelfor the sensor. The slow temporal model for the sensor may be received.It may be determined that the signal from the sensor is inconsistentwith a signal that the slow temporal model indicates is expected fromthe sensor in the situation in which the signal from the sensor wasgenerated. An indication that the sensor is producing anomalous outputmay be generated.

The slow temporal model for the sensor may include signals from thesensor over a period of time. The slow temporal model may be one of astatistical, probabilistic, or machine learning based model for thesignals from the sensor. It may be determined that the signal from thesensor is inconsistent with a signal that the slow temporal modelindicates is expected over a threshold period of time before generatingthe indication that the sensor is producing anomalous output. It may bedetermined that there are no extenuating circumstances causing thesignal from the sensor to be inconsistent with the signal that the slowtemporal model indicates is expected, before the indication that thesensor is producing anomalous output may be generated. An extenuatingcircumstance may include a security system including the sensor beingset to an away mode or a vacation mode.

The sensor may be a motion sensor. It may be determined that the signalfrom the sensor is inconsistent with one of the signals from thesensors. It may be determined that the signal from the motion sensorindicates no motion is detected in an environment monitored by themotion sensor. It may be determined that one of the signals indicatesthe presence of a moving person within the environment. The environmentmay be a room in a structure. The sensor may be a motion sensor, apassive infrared sensor, a low power motion sensor, a light sensor, acamera, a microphone, or an entryway sensor. The notification may be ablocked sensor notification indicating that the sensor is obstructed.The notification may be sent to a computing device associated with auser of a smart home environment, a display within the smart homeenvironment, and a speaker system within the smart home environment.

According to an embodiment of the disclosed subject matter, a means forreceiving a signal from a sensor, a means for determining that thesensor is producing anomalous output based on a means for checking thesignal from the sensor against a signal from a second sensor on the samesensor device as the sensor, a means for checking the signal from thesensor against signals from sensors on additional sensor devices, and ameans for checking the signal from the sensor against a slow temporalmodel for the sensor, a means for generating a notification indicatingthe sensor is producing anomalous output, a means for degrading aconfidence level the sensor, a means for receiving the signal from thesecond sensor on the same sensor device as the sensor, a means fordetermining that the signal from the sensor is inconsistent with thesignal from the second sensor, a means for generating an indication thatthe sensor is producing anomalous output, a means for receiving thesignals from the sensors on the additional sensor devices, a means fordetermining that the signal from the sensor is inconsistent with one ofthe signals from the sensors, a means for determining that the signalsfrom the sensors are consistent with each other, a means for generatingan indication that the sensor is producing anomalous output, a means forreceiving the slow temporal model for the sensor, a means fordetermining that the signal from the sensor is inconsistent with asignal that the slow temporal model indicates is expected from thesensor in the situation in which the signal from the sensor wasgenerated, a means for generating an indication that the sensor isproducing anomalous output, a means for determining that the signal fromthe sensor is inconsistent with a signal that the slow temporal modelindicates is expected over a threshold period of time before generatingthe indication that the sensor is producing anomalous output, a meansfor determining that there are no extenuating circumstances causing thesignal from the sensor to be inconsistent with the signal that the slowtemporal model indicates is expected, before generating the indicationthat the sensor is producing anomalous output, a means for determiningthat the signal from the motion sensor indicates no motion is detectedin an environment monitored by the motion sensor, and a means fordetermining that one of the signals indicates the presence of a movingperson within the environment, are included.

Additional features, advantages, and embodiments of the disclosedsubject matter may be set forth or apparent from consideration of thefollowing detailed description, drawings, and claims. Moreover, it is tobe understood that both the foregoing summary and the following detaileddescription are illustrative and are intended to provide furtherexplanation without limiting the scope of the claims.

Additional features, advantages, and embodiments of the disclosedsubject matter may be set forth or apparent from consideration of thefollowing detailed description, drawings, and claims. Moreover, it is tobe understood that both the foregoing summary and the following detaileddescription are illustrative and are intended to provide furtherexplanation without limiting the scope of the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are included to provide a furtherunderstanding of the disclosed subject matter, are incorporated in andconstitute a part of this specification. The drawings also illustrateembodiments of the disclosed subject matter and together with thedetailed description serve to explain the principles of embodiments ofthe disclosed subject matter. No attempt is made to show structuraldetails in more detail than may be necessary fur a fundamentalunderstanding of the disclosed subject matter and various ways in whichit may be practiced.

FIG. 1 shows an example system suitable for blocked sensor detection andnotification according to an implementation of the disclosed subjectmatter.

FIG. 2 shows an example arrangement suitable for blocked sensordetection and notification according to an implementation of thedisclosed subject matter.

FIG. 3 shows an example arrangement suitable for blocked sensordetection and notification according to an implementation of thedisclosed subject matter.

FIG. 4 shows an example arrangement suitable for blocked sensordetection and notification rides according to an implementation of thedisclosed subject matter.

FIG. 5 shows an example arrangement suitable for blocked sensordetection and notification rides according to an implementation of thedisclosed subject matter.

FIG. 6 shows an example environment suitable for blocked sensordetection and notification rides according to an implementation of thedisclosed subject matter.

FIG. 7 shows an example of a process suitable for blocked sensordetection and notification according to an implementation of thedisclosed subject matter.

FIG. 8 shows an example of a process suitable for blocked sensordetection and notification according to an implementation of thedisclosed subject matter.

FIG. 9 shows an example of a process suitable for blocked sensordetection and notification according to an implementation of thedisclosed subject matter.

FIG. 10 shows an example arrangement suitable for blocked sensordetection and notification according to an implementation of thedisclosed subject matter.

FIG. 11 shows a computing device according to an embodiment of thedisclosed subject matter.

FIG. 12 shows a system according to an embodiment of the disclosedsubject matter.

FIG. 13 shows a system according to an embodiment of the disclosedsubject matter.

FIG. 14 shows a computer according to an embodiment of the disclosedsubject matter.

FIG. 15 shows a network configuration according to an embodiment of thedisclosed subject matter.

DETAILED DESCRIPTION

According to embodiments disclosed herein, blocked sensor detection and.notification may allow a smart home environment to determine when asensor has been blocked or is otherwise producing anomalous output. Thismay allow for a user of the security system to be notified of a blockedsensor, for example so that the user may unblock it. It may also causeconfidence in the blocked sensor to be degraded when the smart homeenvironment uses signals from various sensors through the environment tomake determinations about the status of the environment, such as thenumber of occupants in the environment. The environment may be, forexample, a home, office, apartment, condo, or other structure, and mayinclude a combination of enclosed and open spaces. Signals may bereceived from a sensor in the smart home environment. The sensor may be,for example, a low power motion sensor, such as a passive infraredsensor used for motion detection. The signals from the sensor may becompared to a slow temporal model that has been created for the sensor.The slow temporal model may include a model of the signals received fromthe sensor over a long period of time, for example, o number of days,weeks, months, or years. The signal received from the sensor may bechecked against the slow temporal model. If the signal is determined tobe inconsistent with the stow temporal model, the sensor may bedetermined to be blocked. For example, if no motion is detected by thesensor in a situation where motion is normally detected, such as in thekitchen on a weekday morning, the sensor may be blocked, or may beproducing anomalous output for other reasons, such as a malfunction. Anotification may be generated to a user of the security system toindicate that the sensor is blocked, and confidence in the sensor mayalso be degraded.

The signal from the sensor may also be checked for consistency againstsignals from another sensor that is on the same sensor device. Forexample, a sensor device may include a low-power motion sensor and alight sensor. The low-power motion sensor may generate a signalindicating no motion is detected, while the light sensor may at the sametime generate a signal indicating a change in lighting, indicating thata light switch has been flipped. These signals may be inconsistent, asthe flipping of a light switch should be accompanied by detecting themotion of the person responsible for flipping the switch. This mayresult in the determination that the low power motion sensor is blockedor producing anomalous output.

The signal from the sensor may also be checked for consistency againstsignals from other sensors, on other sensor devices, within theenvironment. For example, a signal from a low power motion sensor may becross-checked against signals from a camera and microphone on adifferent sensor device within the same room. If the signals areinconsistent, the low power motion sensor may be determined to beblocked. For example, the low power motion sensor may generate a signalindicating no motion is detected, while the microphone on the othersensor device in the room may pick up voices, and the camera mayrecognize faces in the room. Because the presence of voices and faces inthe room may be inconsistent with the signal indicating no motion, thelow power motion sensor may be determined to be blocked or producinganomalous output.

The smart home environment may include a hub computing device, which maybe any suitable computing device for managing the smart homeenvironment, including a security system of the smart home environmentand automation system including other functions beyond security. The hubcomputing device may be a controller for a smart home environment. Forexample, the hub computing device may be or include a smart thermostat.The hub computing device also may be another device within the smarthome environment, or may be a separate computing device dedicated tomanaging the smart home environment. The hub computing device may beconnected, through any suitable wired and wireless connections, to anumber of sensors distributed throughout an environment. Some of thesensors may, for example, be motions sensors, including passive infraredsensors used for motion detection, tight detectors, cameras,microphones, entryway sensors, as well as Bluetooth, WiFi, or otherwireless devices used as sensors to detect the presence of devices suchas smartphones, tablets, laptops, or fobs. Sensors may be distributedindividually, or may be combined with other sensors in sensor devices.For example, a sensor device may include a tow power motion sensor and alight sensor, or a microphone and a camera, or any other combination ofavailable sensors.

Signals from the sensors distributed throughout the environment may besent to the hub computing device. The hub computing device may use thesignals received from the sensors to make determinations about theenvironment, including managing the security system and automationfunctions of the smart home environment. For example, the hub computingdevice may use signals received from the sensors to determine how manyoccupants are in a home, based on motion sensing, voice and facerecognition through cameras, and detection of computing devices, such assmartphone or tablets, or fobs associated with residents of the home orguests in the home.

The hub computing device may check signals received from a sensoragainst a slow temporal model for the sensor. The slow temporal modelfor a sensor may be based on the value of the sensor signals over time,and may include patterns in the signal values. The slow temporal modelfor a sensor may be a statistical, probabilistic, or machine learningbased model. For example, a passive infrared sensor may be used as lowpowered motion sensor. The passive infrared sensor may detect heat, andmay detect motion based on the movement of a heat source. The low powermotion sensor may monitor the living room of a house, in which it maydetect movement every weekday morning and evening. This pattern may beincluded in the slow temporal model for the low power motion sensor. If,on a weekday morning, the low power motion sensor does not detect anymovement in the living room, the hub computing device may determine thesignals from the low power motion sensor are inconsistent with the slowtemporal model for the sensor. The hub computing device may determine,based on this inconsistency, that the low power motion sensor has beenblocked, for example, by an object such as a purse or laptop bag, or isotherwise producing anomalous output.

The hub computing device may make the determination that a sensor isblocked or otherwise producing an anomalous signal on the firstoccurrence of an inconsistency between the signals from the sensor andthe slow temporal model for the sensor, or after some threshold numberof inconsistencies. The hub computing device may also determine whetherthere are extenuating circumstances that may explain the inconsistency.For example, if the security system of the smart home environment hasbeen set to an “away” or “vacation” mode, this may indicate that thehome is supposed to be unoccupied, and that the low power motion sensorshould not detect any movement even when the slow temporal modelindicates that movement should be detected. The hub computing device maythen ignore signals from the low power motion sensor that areinconsistent with the slow temporal model for the sensor instead ofdetermining that the low power motion sensor is blocked, until the modeof the security system is changed to a “stay” or “home” mode.

Multiple sensors may be part of the same sensor device. For example, asingle sensor device may include a low power motion sensor and lightsensor, which may detect the light level within an environment. Thesensor device may send signals from all sensors on the sensor device tothe hub computing device. The signals may be checked against each otherfor consistency, to determine if one of the sensors on the sensor deviceis blocked. For example, a low power motion detector on the sensordevice may produce signals indicating that no movement has been detectedin a room in which the sensor device is located. The light sensor mayproduce a signal indicating that the light level in the room hasincreased, going from a dark room to a room illuminated by artificiallight sources. The hub computing device may determine that the signalfrom the low power motion detector, indicating no motion in the room, isinconsistent with the signal from the light detector, indicating a largechange in the light level in the room, as such a change in light levelmay be indicative of a light switch in the room being turned on by aperson. The hub computing device may then determine that low powermotion sensor is blocked or otherwise producing an anomalous signal.

The hub computing device may make the determination that a sensor isblocked or otherwise producing an anomalous signal on the firstoccurrence of an inconsistency between the signals from the sensor andanother sensor on the same sensor device, or after some threshold numberof inconsistencies. The hub computing device may also determine whetherthere are extenuating circumstances that may explain the inconsistency.For example, the light switch for a room may be located outside of theroom, or the lights may be connected to the smart home environment andcontrollable through the hub computing device, allowing them to beswitched on based on a schedule or timer or user interaction even whenno one is moving within the room the lights illuminate.

The smart home environment may include multiple sensors, and multiplesensor devices, placed throughout the environment and connected, eitherwired or wirelessly, to the hub computing device. For example, one roommay include a sensor device with a light sensor and a low power motionsensor, and another sensor device with a camera and a microphone. Thehub computing device may receive signals from all of the sensors on theall of the sensor devices within the environment. For example, the hubcomputing device may receive a signal from the low power motion sensorindicating whether or not motion is detected in the room, and may alsoreceive video signals from the camera and audio signals from themicrophone, which may be used, for example, for voice and facialrecognition.

The hub computing device may cross-check signals from different sensorson different sensor devices against each other to determine if a sensoris blocked or other producing anomalous output. For example, the lowpower motion sensor produces a signal indicating no motion is detectedin a room. The microphone of the other sensor device in the room mayproduce an audio signal including voices, which the hub computing devicemay recognize as belonging to occupants of the home. The hub computingdevice may determine that the signal from the low power motion sensor isinconsistent with the signal from the microphone, and that the low powermotion sensor may be blocked or producing an anomalous signal. Multiplesignals may be cross-checked. For example, the audio signal from themicrophone may also be cross-checked with the video signal from thecamera on the same sensor device. If the video signal does notcorroborate the audio signal, for example, no recognizable faces arefound in the video signal, or no persons can be determined to be in viewof the camera based on the video signal, the hub computing device maydetermine that the signal from microphone is anomalous, rather than thesignal from the low power motion sensor.

The slow temporal model for sensors may also be used to cross-checksensor signals to determine if a sensor is blocked or producinganomalous output. For example, the slow temporal model for a motionsensor may indicate that motion is detected in a room monitored by thelow power motion sensor every evening on weekdays. The room may includeother sensors, either as part of the same or different sensor devices.For example, the room may include another low power motion sensor, and asensor device with a camera and a microphone. The slow temporal modelfor the other low power motion sensor may indicate that motion isdetected the room every evening on weekdays, and the slow temporalmodels from the camera and microphone may indicate facial and voicerecognition of a particular occupant of the home in the room everyevening on weekdays, corresponding to the motion detected by the motionsensors. On a weekday evening, one of the low power motion sensors mayproduce a signal indicating that no motion is detected in the room,inconsistent with the sensor's slow temporal model. If the other lowpower motion sensor, the camera, and the microphone produce signalsconsistent with their slow temporal models for a weekday evening, thehub computing device may determine that the first low power motionsensor is blocked or otherwise producing anomalous output. If the otherlow power motion sensor, the camera, and the microphone produce signalsthat are also not consistent with their slow temporal models, the hubcomputing device may determine that the behavior of the occupant haschanged on the particular evening.

The slow temporal models for the sensors in a smart home environment maybe stored may be stored in any storage accessible to the hub computingdevice. The slow temporal models may include any suitable amount ofsignal data generated by the various sensors, stored in any suitableformat for comparison and pattern detection. For example, the slowtemporal models may be machine learning based models, and whether or nota signal generated by a sensor is consistent with a slow temporal modelfor the sensor may be determined based on confidence levels output by amachine learning system. Slow temporal models may also be statistical,probabilistic, or parameter based. For example, a slow temporal modelfor a low powered motion sensor may be a time-series of signals from thelow motion sensor, with each point in the series indicating whether ornot motion was detected.

When the hub computing device has determined that a sensor is blocked oris otherwise producing anomalous output, the hub computing device maynotify a user of the smart home environment. For example, the hubcomputing device may send a message, via email, SMS, MMS, or applicationnotification, to a computing device associated with a user of the smarthome environment, such as a smartphone, tablet, laptop, or wearablecomputing device. The hub computing device may display a message, forexample, on a display of the hub computing device or other display thatis part of the smart home environment, such as a television or displayon a smart thermostat.

The hub computing device may use signals from the various sensors in thesmart home environment to make determinations about the state of theenvironment. For example, the hub computing device may determine anoccupancy model of a home based on signals from sensors throughout thehome. When the hub computing device has determined that a sensor isblocked or otherwise producing anomalous output, the hub computingdevice may degrade the confidence level of the sensor. Signals from asensor with a degraded confidence level may have less of an impact ondeterminations made by the hub computing device about the environment,such as, for example, the number and location of occupants in theenvironment. The confidence level in the sensor may be degraded overtime. For example, every instance of inconsistency between signals froma sensor and the slow temporal model for the sensor may result in asmall decrease in the confidence level for the sensor, so that a numberof detected inconsistencies may be required before the confidence levelin the sensor is severely degraded. Likewise, the confidence level of asensor may be restored when the signals from the sensor show consistencywith the slow temporal model over a number of instances.

Sensors in the smart home environment may send indications to the hubcomputing device actively or passively. For example, a low power motionsensor may actively produce an output signal when motion is and is notdetected, with the signal including the indication of whether or notmotion was detected. Alternatively, the low power motion sensor may onlyproduce active output when motion is detected, with the output being thesignal that motion was detected, and may otherwise produce no outputwhen not motion is detected, with the lack of output acting a signalthat motion was not detected. This may allow the tow power motion sensorto operate using less power. The hub computing device may interpret thelack of active output from a low power motion sensor as a signalindicating that no motion has been detected by the sensor. Anomalousoutput for a low power motion sensor may include not sending any outputto the hub computing device or sending output including a signalindicating that there is no motion detected when there is motion in theroom, or sending a signal indicating motion in the room when there is nomotion.

FIG. 1 shows an example system suitable for blocked sensor detection andnotification according to an implementation of the disclosed subjectmatter. A hub computing device 100 may include a signal receiver 110, asignal cross-checker 120, a model checker 130, and storage 140. The hubcomputing device 100 may be any suitable device, such as, for example, acomputer 20 as described in FIG. 14, for implementing the signalreceiver 110, the signal cross-checker 120, the model checker 130, andstorage 140. The hub computing device 100 may be, for example, acontroller 73 as described in FIG. 12. The hub computing device 100 maybe a single computing device, or may include multiple connectedcomputing devices, and may be, for example, a smart thermostat, othersmart sensor, smartphone, tablet, laptop, desktop, smart television,smart watch, or other computing device that may be able to act as a hubfor a smart home environment, which may include a security system andautomation functions. The smart home environment may be controlled fromthe hub computing device 100. The hub computing device 100 may alsoinclude a display. The signal receiver 110 may be any suitablecombination of hardware or software for receiving signals generated bysensors that may be part of the smart home environment and may beconnected to the hub computing device 100. The signal cross-checker 120may be any suitable combination of hardware and software forcross-checking signals from various signals against each other forconsistency. The model checker 130 may be any suitable hardware andsoftware for comparing signals received from sensors with a slowtemporal model 141 for the sensor stored in the storage 140. The slowtemporal models 141 may be stored the storage 140 in any suitablemanner.

The hub computing device 100 may be any suitable computing device foracting as the hub of a smart home environment. For example, the hubcomputing device 100 may be a smart thermostat, which may be connectedto various sensors throughout an environment as well as to varioussystems within the environment, such as HVAC systems, or it may beanother device within the smart home environment. The hub computingdevice 100 may include any suitable hardware and software interfacesthrough which a user may interact with the hub computing device 100. Forexample, the hub computing device 100 may include a touchscreen display,or may include web-based or app based interface that can be accessedusing another computing device, such as a smartphone, tablet, or laptop.The hub computing device 100 may be located within the same environmentas the smart home environment it controls, or may be located offsite. Anonsite hub computing device 100 may use computation resources from othercomputing devices throughout the environment or connected remotely, suchas, for example, as part of a cloud computing platform. The hubcomputing device 100 may be used to arm a security system of the smarthome environment, using, for example, an interface on the hub computingdevice 100. The security system may be interacted with by a user in anysuitable matter, including through a touch interface or voice interface,and through entry of a PIN, password, or pressing of an “arm” button onthe hub computing device 100.

The hub computing device 100 may include a signal receiver 110. Thesignal receiver 110 may be any suitable combination of hardware andsoftware for receiving signals from sensors connected to the hubcomputing device 100. For example, the signal receiver 110 may receivesignals from any sensors distributed throughout a smart homeenvironment, either individually or as part of sensor devices. Thesignal receiver 110 may receive any suitable signals from the sensors,including, for example, audio and video signals, signals indicatinglight levels, signals indicating detection or non-detection of motion,signals whether entryways are open, closed, opening, closing, orexperiencing any other form of displacement, signals indicating thecurrent climate conditions within and outside of the environment, smokeand carbon monoxide detection signals, and signals indicating thepresence or absence of occupants in the environment based on Bluetoothor WiFi signals and connections from electronic devices associated withoccupants or fobs carried by occupants. The signal receiver 110 may passreceived signals to other components of the hub computing device 100 forfurther processing, such as, for example, detection of tripped motionand entryway sensors and use in automation and security determinations,and for storage. The signal receiver 110 may also be able to receive, orto associate with a received signal, an identification for the sensorfrom which the signal was received. This may allow the signal receiver110 to distinguish which signals are being received from which sensorsthroughout the smart home environment. For example, a low power motionsensor may send a sensor identification to the signal receiver 110 whenactively outputting a signal indicating motion has been detected. Thelow power motion sensor may not actively output a signal when no motionis detected, so the signal receiver may be able to determine that thelack of active output from the low power motion sensor is a signalindicating no motion was detected, and may associate this signal withthe identity of the low power motion sensor from which no output wasreceived.

The hub computing device 100 may include a signal cross-checker 120. Thesignal cross-checker 120 may be any suitable combination of hardware andsoftware for cross-checking and correlating signals from varioussensors. The signal cross-checker 120 may check signals received by thesignal checker 110 for consistency with each other. The signals may befrom different sensors on the same sensor device, or from differentsensors on different sensor devices. For example, the signalcross-checker 120 may check a signal received from a low powered motionsensor on a sensor device against a signal received from a light sensoron the same sensor device to determine if the signals are consistent orif one of the sensors is producing anomalous output. The signal-crosschecker 120 may check a signal received from a low powered motion sensoron first sensor device against signals received from sensors, such as acamera and a microphone, on a second sensor device to determine if thesignals are consistent or if one of the sensors is producing anomalousoutput.

The hub computing device 100 may include a model checker 130. The modelchecker 130 may be any suitable combination of hardware and software forchecking signals from various sensors against the slow temporal models141. The slow temporal models 141 may include slow temporal models forthe various sensors in the smart home environment. The model checker 130may check a signal received by the signal receiver 110 against the slowtemporal model 141 for the sensor that generated the signal, todetermine if the signal is consistent with the slow temporal model 141.The model checker 130 may also cross-check signals from various sensorsagainst the slow temporal models 141, in conjunction with the signalcross-checker 120 checking the signals against each other. The modelchecker 130 may determine if only one of the various signals areinconsistent with the slow temporal models 141 for their sensor, whichmay indicate anomalous output from the inconsistent sensor, or if all ofthe signals are inconsistent, which may indicate a change from thenormal pattern observed in the part of the environment monitored by thevarious sensors.

The storage 140 may be any suitable storage hardware connected to thehub computing device 100, and may store the slow temporal model 14 inany suitable manner. For example, the storage 140 may be a component ofthe hub computing device, such as a flash memory module or solid statedisk, or may be connected to the hub computing device 100 through anysuitable wired or wireless connection. It may be a local storage, i.e.,within the environment within which the hub computing device operates,or it may be partially or entirely operated by a remote service, such asa cloud-based monitoring service as described in further detail herein.Any number of slow temporal models 141 may be stored, each of which maybe a representation of signals received from a particular sensor overtime. A slow temporal model 141 for a sensor may be stored in anysuitable format, including, for example, as a set of parameters orconditional clauses, or a time-series of recorded signals, or as weightsfor a suitable machine learning system. The slow temporal model 141 fora sensor may be based on, for example, signals received from the sensorby the signal receiver 110 over any suitable period of time, such as,for example, days, weeks, months, or years.

FIG. 2 shows an example arrangement suitable for blocked sensordetection and notification according to an implementation of thedisclosed subject matter. Tho hub computing device 100 may be the hub,or controller, for a smart home environment. Various sensor devicesthroughout the environment may be connected to the hub computing device100. Each sensor device may have any suitable assortment of sensors. Forexample, the sensor devices 210, 220, 230, and 240 may be connected tothe hub computing device 100. The sensor device 210 may include a motionsensor 212 and a light sensor 214. The sensor device 220 may include amotion sensor 222, a camera 224, and a microphone 226. The sensor device230 may include a camera 232 and a microphone 234. The sensor device 240may include an entry sensor 242. The motions sensors 212 and 222 may beany suitable sensors for detecting motion in an environment, such as,for example, a low power motion sensor using a passive infrared sensorto detect the motion of heat. The light sensor 214 may be any suitablesensor for detecting light levels within an environment. The entrywaysensor 242 may be any suitable type of sensor, such as contact sensors,including magnetic contact sensors, and tilt sensors, for detecting whenan entryway is open. For example, the entryway sensor 242 may be asensor attached to a bedroom window in a home, and may detect when thebedroom window has been moved in any way, for example moved towards anopen or closed position, and may also measure vibrations or impactsexperienced by the window.

The sensors of the sensors devices 210, 220, 230, and 240 may generatesignals that maybe received by the signal receiver of the hub computingdevice 100. The signals may be the product of active output the sensors,for example, a video or audio signal produced by the camera 224 ormicrophone 226, or may be the result of a sensor not generating anyoutput, for example, a lack of output from the motion sensor 212 when nomotion is detected.

The hub computing device 100 may also be connected, in any suitablemanner, to a user computing device 280. The user computing device 280may be any suitable computing device, such as, for example, asmartphone, tablet, laptop, or smartwatch or other wearable computingdevice, which a user may use to interface with the hub computing device100 and control the security system. The hub computing device 100 may beable to send notifications, alerts or requests to the user computingdevice 280, either through a direct connection, such as LAN connection,or through a WAN connection such as the Internet. This may allow theuser of the user computing device 280 to monitor and manage the smarthome environment even when the user is not physically near the hubcomputing device 100. For example, when the signal cross-checker 120determines that a sensor, such as the motion sensor 212, is blocked orotherwise producing anomalous output, the hub computing device 100 maysend a notification, alert, or request for action to the user computingdevice 280. The user computing device 280 may be used by the user torespond to such a notification, alert, or request for action, forexample, by providing an indication to the hub computing device 100 ofan action to take in regards to the blocked or malfunctioning sensor.

FIG. 3 shows an example arrangement suitable for blocked sensordetection and notification according to an implementation of thedisclosed subject matter. A signal indicating that no motion has beendetected may be received by the signal receiver 110 from the motionsensor 212 of the sensor device 210. For example, the motion sensor 212may output a signal indicating that no motion has been detected, or maynot produce any output, which may be interpreted by the signal receiver110 as a signal that no motion has been detected. The signal may includean identification for the motion sensor 212, or the signal receiver 110may otherwise correlate the signal with the motion sensor 212.

The model checker 130 may receive the signal indicating no motion fromthe motion sensor 212 and the identification of the motion sensor 212from the signal receiver 110. The model checker 130 may check the slowtemporal model 141 for the motion sensor 212, based on the receivedidentification for the motion sensor 212, to determine whether thesignal is consistent with the slow temporal model 141. Consistency withthe slow temporal model 141 may be checked in any suitable manner. Forexample, if the slow temporal model 141 for the motion sensor 212 isstored as weights for a machine learning system, the signal from themotion sensor 212 may be input into an appropriate machine learningsystem using the stored weights, which may result in the output of, forexample, a confidence level that the signal is consistent with the slowtemporal model 141.

The model checker 130 may determine that the signal indicating no motionfrom the motion sensor 212 is inconsistent with the slow temporal model141 for the motion sensor 212. For example, it may be a 9:00 am on aweekday morning, and the slow temporal model 141 for the motion sensor212 may indicate that motion should be detected at that time, as it hasbeen on at 9:00 am on all previous weekday mornings. A signal from themotion sensor 212 indicating that no motion is detected may then beinconsistent with the slow temporal model 141. If there are noextenuating circumstances, for example, the smart home environment hasbeen set to an “away” or “vacation” mode, the model checker 130 maydetermine that the motion sensor 212 is blocked or otherwise producinganomalous output. This determination may made after the firstinconsistency is detected, or may require a number of consecutiveinconsistencies, for example, to account for slight variations in theschedule of the occupant whose motion is normally detected at 9:00 am onweekdays. For example, the model checker 130 may not determine that themotion sensor 212 is blocked or other producing anomalous output untilsome time period, for example, 30 minutes, have passed, during which themotion sensor 212 produces a signal of no motion detected that isinconsistent with the slow temporal model 141 for the motion sensor 212.

The model checker 130 may generate a blocked sensor indication. Theblocked sensor indication may indicate the motion sensor 212 is eitherblocked by an object or is otherwise malfunctioning or out of position,resulting in anomalous output. The blocked sensor indication may be sentto any suitable component of the hub computing device 100, such as thesignal receiver 110. The signal receiver 110 may use the blocked sensorindication to degrade the confidence level for the motion sensor 212.The signal receiver 110 may also generate a notification of the blockedsensor, which may be sent to a user on the user computing device 280,may be displayed on a display in the smart home environment connected tothe hub computing device 100 or part of the hub computing device 100, ormay be audibly communicated to a user through a speaker that is acomponent of the smart home environment.

The notification of the blocked sensor may be sent to the user based onthe detection of the user's presence or absence in the environment. Forexample, if the user is not detected within the environment, thenotification may be sent to the user computing device 280, which may be,for example, a smartphone or wearable computing device associated withthe user. If the user is detected within the environment, thenotification may be displayed on the nearest screen to the user.

The notification may include an identification of the motion sensor 212,and a report indicating why the motion sensor 212 was determined to beblocked or otherwise producing anomalous output, so that the user mayattempt to correct the issue by, for example, removing an objectblocking the motion sensor 212, re-positioning the motion sensor 212, orreplacing the motion sensor 212 if it is defective.

FIG. 4 shows an example arrangement suitable for blocked sensordetection and notification according to an implementation of thedisclosed subject matter. A signal indicating that no motion has beendetected may be received by the signal receiver 110 from the motionsensor 212 of the sensor device 210. The signal may include anidentifier for the motion sensor 212, or the signal receiver 110 mayotherwise correlate the signal with the motion sensor 212. The motionsensor 222, camera 224, and microphone 226 of the sensor device 220 maysend signals including video from the camera 224, audio from themicrophone 226, and an indication of motion detected from the motionsensor 222 to the signal receiver 110. The signals may include anidentification of the motion sensor 222, the camera 224, and themicrophone 226. The sensor device 220 may monitor the same area of theenvironment as the motion sensor 212. For example, sensor device 210 andthe sensor device 220 may be placed in the same room of a home.

The signal receiver 110, or another suitable component of the hubcomputing device 100, may process the audio and video signals from themicrophone 226 and the camera 224 in any suitable manner. For example,the voice and facial recognition may be performed on the audio and videosignals. The video signals may also be processed to recognize thepresence or motion of persons in the video.

The signal cross-checker 120 may check the signals from the motionsensors 212 and 222, the camera 224, and the microphone 226, againsteach other to determine if they are consistent. For example, since thesensor device 210 and the sensor device 220 are in the same room of ahome, if the motion sensor 222 detects motion, the motion sensor 212 mayalso detect motion. 117 the audio signal from the microphone 226includes a voice recognized as belonging to an occupant of the home,then it should be likely that the motion sensors 212 and 222 will alsodetect motion, unless the voice is originating from a different room.Similarly if the video signal from the camera 224 includes a facerecognized as belonging to an occupant of the home, then the motionsensors 212 and 222 should detect motion of that occupant in the room.

The signal cross-checker 120 may determine that the signal indicating nomotion from the motion sensor 212 is inconsistent with the signals fromthe motion sensor 222, the camera 224, and the microphone 226. Forexample, the motion sensor 212 may indicate that no motion is detected,and the audio and video signals from the camera 224 and the microphone226 may include a voice and face recognized as belonging to an occupantof the home. This may indicate that the occupant is present in the room,and the motion sensor 212 has failed to pick up the movement of theoccupant. The signal cross-checker 120 may determine that the motionsensor 212 is blocked or otherwise producing anomalous output.

The signal cross-checker 120 may check any suitable signal from anysuitable sensor against any other suitable signals. For example, thesignal cross-checker 120 may check signals from the entryway sensor 242of the sensor device 240 and the camera 232 and microphone 234 againstof the sensor device 230 against the signals from the motion sensor 212if the sensor device 240 is positioned at a suitable entryway relativeto the position of the motion sensor 212, and the sensor device 230 ispositioned in the same area as the sensor device 210. For example,entryway sensor 242 may monitor a door to a room in which the sensordevice 210 is positioned, and the sensor device 230 may be positioned inthe room with the sensor device 210. The signal cross-checker 120 mayreceive a signal from the entryway sensor 242 indicating that the doorto the room has been opened, audio and video signals from the microphone234 and the camera 232 in which the voice and face of an occupant arerecognized, and a signal from the motion sensor 212 that no motion hasbeen detected. The signal cross-checker 120 may determine that thesesignals are inconsistent, as the motion sensor 212 should detect motionof the occupant who opened the door and is talking in the room. Thesignal cross-checker 120 may determine that the motion sensor 212 isblocked or otherwise producing anomalous output.

The signal cross-checker 120 may generate a blocked sensor indication.The blocked sensor indication may indicate the motion sensor 212 iseither blocked by an object or is otherwise malfunctioning or out ofposition, resulting in anomalous output. The blocked sensor indicationmay be sent to any suitable component of the hub computing device 100,such as the signal receiver 110. The signal receiver 110 may use theblocked sensor indication to degrade the confidence level for the motionsensor 212. The signal receiver 110 may also generate a. notification ofthe blocked sensor, which may be sent to a user on the user computingdevice 280, may be displayed on a display in the smart home environmentconnected to the hub computing device 100 or part of the hub computingdevice 100, or may be audibly communicated to a user through a speakerthat is a component of the smart home environment.

FIG. 5 shows an example arrangement suitable for blocked sensordetection and notification according to an implementation of thedisclosed subject matter. A signal indicating that no motion has beendetected may be received by the signal receiver 110 from the motionsensor 212 of the sensor device 210. The signal may include anidentifier for the motion sensor 212, or the signal receiver 110 mayotherwise correlate the signal with the motion sensor 212. The lightsensor 214 of the sensor device may send a signal indicating a change inlight level to the signal receiver 110, along with an identifier of thelight sensor 214.

The signal cross-checker 120 may check the signals from the motionsensor 212 and the light sensor 214, against each other to determine ifthey are consistent. For example, the motion sensor 212 may indicatethat no motion is detected, and the light sensor 214 may indicate thatthe light level in the room has changed, for example, in a mannerconsistent with a light switch in a room being turned on. The signalsfrom the motion sensor 212 and the light sensor 214 on the sensor device210 may be determined to be inconsistent. The signal cross-checker 120may determine that the motion sensor 212 is blocked or otherwiseproducing anomalous output.

The signal cross-checker 120 may generate a blocked sensor indication.The blocked sensor indication may indicate the motion sensor 212 iseither blocked by an object or is otherwise malfunctioning or out ofposition, resulting in anomalous output. The blocked sensor indicationmay be sent to any suitable component of the hub computing device 100,such as the signal receiver 110. The signal receiver 110 may use theblocked sensor indication to degrade the confidence level for the motionsensor 212. The signal receiver 110 may also generate a notification ofthe blocked sensor, which may be sent to a user on the user computingdevice 280, may be displayed on a display in the smart home environmentconnected to the hub computing device 100 or part of the hub computingdevice 100, or may be audibly communicated to a user through a speakerthat is a component of the smart home environment.

The signal cross-checker 120 may also work in conjunction with the modelchecker 130. For example, if a signal from the motion sensor 212 isfound to be inconsistent with a signal from light sensor 214 on the samesensor device 210, the signal cross-checker 120 may not be able todetermine which of the motion sensor 212 and the light sensor 214 isproducing the anomalous output. The model checker 130 may check thesignals from the motion sensor 212 and the light sensor 214 againsttheir slow temporal models 141. If one of the signals, for example, thesignal from the motion sensor 212, is inconsistent with the slowtemporal model 141 while the other signal, for example, the signal fromthe light sensor 214, is consistent with the slow temporal 141, themodel checker 130 may determine that the sensor that generated signalthat is inconsistent with the slow temporal model 141 is blocked orotherwise producing anomalous output.

FIG. 6 shows an example environment suitable for blocked sensordetection and notification rides according to an implementation of thedisclosed subject matter. The sensor device 210 and the sensor device220 may be used to monitor the same room 600, which may be, for example,the living room of a home. The sensor drive 210 and the sensor device220 may be positioned differently, for example, based on their includedsensors. For example, the sensor device 220 may be positioned on a highshelf overlooking the room 600 to allow the camera 224 a better view ofthe entire room 600. The sensor device 210, including the motion sensor212, may be positioned on the floor on the room 600, near an entryway610, in order to allow the motion sensor 212 to better register when aperson enters the room 600 through the entryway 610.

An obstruction 620 may be placed in front of the sensor device 210. Theobstruction 620 may be, for example, a box, bag, backpack, purse, laptopcase, or any other item that may obstruct the motion sensor 212. Themotion sensor 212 may be a passive infrared sensor, and may be unable todistinguish between the obstruction 620 and a lack of motion in the room600. Signals from the motion sensor 212 to the signal receiver 110 mayindicate no motion in the room 600 for as long as the obstruction 620 isin front of the sensor device 210. To detect that the motion sensor 212is blocked by the obstruction 620, the signals from the motion sensor212 may cross-checked against the signals from the sensor device 220,which may not be blocked, checked against the slow temporal model 141for the motion sensor 212, which may indicate that the motion sensor 212is not detecting motion in situations where motion is usually detected,and checked against signals from the tight sensor 214, which may operateproperly despite the obstruction 620. Once the motion sensor 212 isdetermined to be blocked, a notification may be sent to a user in anysuitable manner so that the user may check the motion sensor 212, andremove the obstruction 620.

FIG. 7 shows an example of a process suitable for blocked sensordetection and notification according to an implementation of thedisclosed subject matter. At 700, a signal may be received from asensor. For example, the signal receiver 110 of the hub computing device100 may receive a signal from the motion sensor 212 of the sensor device210, which may be part of a smart home environment. The signal may beactively output by the motion sensor 212, or may be interpreted from alack out active output from the motion sensor 212. The signal mayindicate, for example, that the motion sensor 212 has not detected anymotion. The signal may also include an identification of the motionsensor 212, either explicitly, or based on a correlation by the signalreceiver 110.

At 702, the signal may be checked against a slow temporal model for thesensor. For example, the model checker 130 may check the signal receivedfrom the motion sensor 212 against the slow temporal model 141 for themotion sensor 212. The stow temporal model 141 may be, for example, astatistical, probabilistic, or machine learning based model of signalsreceived from the motion sensor 212 over a period of time. Theidentification of the motion sensor 212 may be used to identify thecorrect slow temporal model 141.

At 704, it may be determined that the signal is inconsistent with theslow temporal mode. For example, it may be a weekday morning, and thesignal received from the motion sensor 212 may indicate that no motionhas been detected. The slow temporal model 141 may indicate that insimilar situations in the past, for example, on previous weekdaymornings, the motion sensor 212 detected motion. The signal from themotion sensor 212 indicating no motion may be inconsistent with the slowtemporal model 141. The determination of inconsistency may be made inany suitable manner. For example, the signal may be determined to beinconsistent if it is inconsistent with the slow temporal model 1411once, or may only be determined to be inconsistent if it is inconsistentwith the slow temporal model 141 over some period of time, or overseveral different periods of time. For example, the slow temporal model141 may indicate that motion is normally detected on weekday morningsfrom 9:00 am to 9:30 am. If the signal from the motion sensor 212indicates no motion from 9:00 am to 9:15 am, but then indicates motionfrom 9:25 am to 9:40 am, the signal may not be determined toinconsistent with the slow temporal model 141 The signal may also bedetermined to be inconsistent with the slow temporal model 141 overseveral different periods of time. For example, the motion sensor 212may generate a signal indicating no motion detected over the course ofseveral weekday mornings, when the slow temporal model 1411 indicatesthat motion should be detected, before the signal is determined to beinconsistent with the slow temporal model 141.

At 706, a blocked sensor indication may be generated. For example, themodel checker 130 may have determined that the signal from the motionsensor 212 is inconsistent with the slow temporal model 141. This mayindicate that the motion sensor 212 is blocked or otherwise producinganomalous output. A blocked sensor indication may be generated by themodel checker 130 and sent to the signal receiver 110. The signalreceiver 110 may degrade the confidence level of the motion sensor 212based on the blocked sensor indication.

At 708, a blocked sensor notification may be sent. For example, thesignal receiver 110, or other suitable component the hub computingdevice 100 may send a notification indicating that the motion sensor 212is blocked to a user of the smart home environment. The notification maybe sent to the user computing device 280, displayed on a display of thehub computing device 100 or other display connected to the smart homeenvironment, transmitted through a speaker of the smart homeenvironment, or sent in any other suitable manner. The notification mayidentify the motion sensor 212, so that the user may attempt to unblockor otherwise fix or replace the motion sensor 212.

FIG. 8 shows an example of a process suitable for blocked sensordetection and notification according to an implementation of thedisclosed subject matter. At 800, a signal may be received from a firstsensor on a sensor device. For example, the signal receiver 110 of thehub computing device 100 may receive a signal from the motion sensor 212of the sensor device 210, which may be part of a smart home environment.The signal may be actively output by the motion sensor 212, or may beinterpreted from a lack out active output from the motion sensor 212.The signal may indicate, for example, that the motion sensor 212 has notdetected any motion. The signal may also include an identification ofthe motion sensor 212, either explicitly, or based on a correlation bythe signal receiver 110.

At 802, a signal may be received from a second sensor on the sensordevice. For example, the signal receiver 110 of the hub computing device100 may receive a signal from the light sensor 214 of the sensor device210.

At 804, the signal from the first sensor may be cross-checked againstthe signal from the second sensor. For example, the signal cross-checker120 may check the signal received from the motion sensor 212 against thesignal received from the light sensor 214.

At 806, it may be determined that the signal from the first sensor isinconsistent with the signal from the second sensor of the sensordevice. For example, the signal received from the motion sensor 212 mayindicate that no motion has been detected. The signal received from thelight sensor 214 may indicate that the light level in the room with thesensor device 210 has changed, for example, going from dark to lightfrom artificial light sources. This may indicative of a light switch inthe room being switched on. The signal from the motion sensor 212 may bedetermined to be inconsistent with the signal from the light sensor 214,as a person may need to be in the room in order to turn the lights on,in which case the motion sensor 212 should have registered the motion ofthat person.

At 808, a blocked sensor indication may be generated. For example, themodel checker 130 may have determined that the signal from the motionsensor 212 is inconsistent with the signal from the light sensor 214.This may indicate that the motion sensor 212 is blocked or otherwiseproducing anomalous output. A blocked sensor indication may be generatedby the model checker 130 and sent to the signal receiver 110. The signalreceiver 110 may degrade the confidence level of the motion sensor 212based on the blocked sensor indication.

At 810, a blocked sensor notification may be sent. For example, thesignal receiver 110, or other suitable component the hub computingdevice 100 may send a notification indicating that the motion sensor 212is blocked to a user of the smart home environment. The notification maybe sent to the user computing device 280, displayed on a display of thehub computing device 100 or other display connected to the smart homeenvironment, transmitted through a speaker of the smart homeenvironment, or sent in any other suitable manner. The notification mayidentify the motion sensor 212, so that the user may attempt to unblockor otherwise fix or replace the motion sensor 212.

FIG. 9 shows an example of a process suitable for blocked sensordetection and notification according to an implementation of thedisclosed subject matter. At 900, a signal may be received from a sensoron a first sensor device. For example, the signal receiver 110 of thehub computing device 100 may receive a signal from the motion sensor 212of the sensor device 210, which may be part of a smart home environment.The signal may be actively output by the motion sensor 212, or may beinterpreted from a lack out active output from the motion sensor 212.The signal may indicate, for example, that the motion sensor 212 has notdetected any motion. The signal may also include an identification ofthe motion sensor 212, either explicitly, or based on a correlation bythe signal receiver 110.

At 902, a signal may be received from a sensor on a second sensordevice. For example, the signal receiver 110 of the hub computing device100 may receive a signal from the camera 224 of the sensor device 220.The sensor device 220 may be located in the same environment, forexample, room, as the sensor device 210, so that signals from sensors onthe sensor device 210 and the sensor device 220 may be correlated. Thesignal from the camera 224 may be a video signal that may be used, forexample, for facial recognition of known occupants on the home.

At 904, the signal from the sensor of the first sensor device may becross-checked against the signal from the sensor of the second sensordevice. For example, the signal cross-checker 120 may check the signalreceived from the motion sensor 212 against the signal received from thecamera 224.

At 906, it may be determined that the signal from the first sensor driveis inconsistent with the signal from the second sensor device. Forexample, the signal received from the motion sensor 212 may indicatethat no motion has been detected. The signal received from video signalreceived from the camera 224 may include a face of an occupant of thehome, recognized by, for example, facial recognition performed by thesignal receiver 110. The signal from the motion sensor 212 may bedetermined to be inconsistent with the signal from the camera 224, as aperson may need to be in the room in order for their face to be recordedup by the camera 224 and recognized, in which case the motion sensor 212should have registered the motion of that person.

At 908, a blocked sensor indication may be generated. For example, themodel checker 130 may have determined that the signal from the motionsensor 212 is inconsistent with the signal from the camera 224. This mayindicate that the motion sensor 212 is blocked or otherwise producinganomalous output. A blocked sensor indication may be generated by themodel checker 130 and sent to the signal receiver 110. The signalreceiver 110 may degrade the confidence level of the motion sensor 212based on the blocked sensor indication.

At 910, a blocked sensor notification may be sent. For example, thesignal receiver 110, or other suitable component the hub computingdevice 100 may send a notification indicating that the motion sensor 212is blocked to a user of the smart home environment. The notification maybe sent to the user computing device 280, displayed on a display of thehub computing device 100 or other display connected to the smart homeenvironment, transmitted through a speaker of the smart homeenvironment, or sent in any other suitable manner. The notification mayidentify the motion sensor 212, so that the user may attempt to unblockor otherwise fix or replace the motion sensor 212.

FIG. 10 shows an example arrangement suitable for blocked sensordetection and notification according to an implementation of thedisclosed subject matter. The signal receiver 110 may send blockedsensor notifications to a user of the security system in any suitablemanner. For example, a blocked sensor notification may be sent to thedisplay of the user computing device 280, a display 1020 of the hubcomputing device 100 or other computing device within the smart homeenvironment, or to a speaker 1030 within the smart home environment. Theblocked sensor notification may be sent any number of displays orspeakers, which may be chosen, for example, based on their proximity tothe user the blocked sensor notification is sent to. For example, if theuser is currently an occupant of the environment and is near the speaker1030, the speaker 1030 may be used to communicate the blocked sensornotification to the user. If the user is absent from the environment,the blocked sensor notification may be sent to the user computing device280, which may be, for example, the user's smartphone. The blockedsensor notification may include, for example, a notification 1010, whichmay explain in written form or verbally which sensor of the securitysystem has been determined to be blocked or producing anomalous output.

Embodiments disclosed herein may use one or more sensors. In general, a“sensor” may refer to any device that can obtain information about itsenvironment. Sensors may be described by the type of information theycollect. For example, sensor types as disclosed herein may includemotion, smoke, carbon monoxide, proximity, temperature, time, physicalorientation, acceleration, location, and the like. A sensor also may bedescribed in terms of the particular physical device that obtains theenvironmental information. For example, an accelerometer may obtainacceleration information, and thus may be used as a general motionsensor and/or an acceleration sensor. A sensor also may be described interms of the specific hardware components used to implement the sensor.For example, a temperature sensor may include a thermistor,thermocouple, resistance temperature detector, integrated circuittemperature detector, or combinations thereof. In some cases, a sensormay operate as multiple sensor types sequentially or concurrently, suchas where a temperature sensor is used to detect a change in temperature,as well as the presence of a person or animal.

In general, a “sensor” as disclosed herein may include multiple sensorsor sub-sensors, such as where a position sensor includes both a globalpositioning sensor (GPS) as well as a wireless network sensor, whichprovides data that can be correlated with known wireless networks toobtain location information. Multiple sensors may be arranged in asingle physical housing, such as where a single device includesmovement, temperature, magnetic, and/or other sensors. Such a housingalso may be referred to as a sensor or a sensor device. For clarity,sensors are described with respect to the particular functions theyperform and/or the particular physical hardware used, when suchspecification is necessary for understanding of the embodimentsdisclosed herein.

A sensor may include hardware in addition to the specific physicalsensor that obtains information about the environment. FIG. 11 shows anexample sensor as disclosed herein. The sensor 60 may include anenvironmental sensor 61, such as a temperature sensor, smoke sensor,carbon monoxide sensor, motion sensor, accelerometer, proximity sensor,passive infrared (PIR) sensor, magnetic field sensor, radio frequency(RF) sensor, light sensor, humidity sensor, or any other suitableenvironmental sensor, that obtains a corresponding type of informationabout the environment in which the sensor 60 is located. A processor 64may receive and analyze data obtained by the sensor 61, controloperation of other components of the sensor 60, and processcommunication between the sensor and other devices. The processor 64 mayexecute instructions stored on a computer-readable memory 65. The memory65 or another memory in the sensor 60 may also store environmental dataobtained by the sensor 61. A communication interface 63, such as a orother wireless interface, Ethernet or other local network interface, orthe like may allow for communication by the sensor 60 with otherdevices. A user interface (UI) 62 may provide information and/or receiveinput from a user of the sensor. The UI 62 may include, for example, aspeaker to output an audible alarm when an event is detected by thesensor 60. Alternatively, or in addition, the UI 62 may include a lightto be activated when an event is detected by the sensor 60. The userinterface may be relatively minimal, such as a limited-output display,or it may be a full-featured interface such as a touchscreen. Componentswithin the sensor 60 may transmit and receive information to and fromone another via an internal bus or other mechanism as will be readilyunderstood by one of skill in the art. One or more components may beimplemented in a single physical arrangement, such as where multiplecomponents are implemented on a single integrated circuit. Sensors asdisclosed herein may include other components, and/or may not includeall of the illustrative components shown.

Sensors as disclosed herein may operate within a communication network,such as a conventional wireless network, and/or a sensor-specificnetwork through which sensors may communicate with one another and/orwith dedicated other devices. In some configurations one or more sensorsmay provide information to one or more other sensors, to a centralcontroller, or to any other device capable of communicating on a networkwith the one or more sensors. A central controller may be general-orspecial-purpose. For example, one type of central controller is a homeautomation network that collects and analyzes data from one or moresensors within the home. Another example of a central controller is aspecial-purpose controller that is dedicated to a subset of functions,such as a security controller that collects and analyzes sensor dataprimarily or exclusively as it relates to various securityconsiderations for a location. A central controller may be locatedlocally with respect to the sensors with which it communicates and fromwhich it obtains sensor data, such as in the case where it is positionedwithin a home that includes a home automation and/or sensor network.Alternatively or in addition, a central controller as disclosed hereinmay be remote from the sensors, such as where the central controller isimplemented as a cloud-based system that communicates with multiplesensors, which may be located at multiple locations and may be local orremote with respect to one another.

FIG. 12 shows an example of a sensor network as disclosed herein, whichmay be implemented over any suitable wired and/or wireless communicationnetworks. One or more sensors 71, 72 may communicate via a local network70, such as a Wi-Fi or other suitable network, with each other and/orwith a controller 73. The controller may be a general-or special-purposecomputer. The controller may, for example, receive, aggregate, and/oranalyze environmental information received from the sensors 71, 72. Thesensors 71, 72 and the controller 73 may be located locally to oneanother, such as within a single dwelling, office space, building, room,or the like, or they may be remote from each other, such as where thecontroller 73 is implemented in a remote system 74 such as a cloud-basedreporting and/or analysis system. Alternatively or in addition, sensorsmay communicate directly with a remote system 74. The remote system 74may, for example, aggregate data from multiple locations, provideinstruction, software updates, and/or aggregated data to a controller 73and/or sensors 71, 72.

For example, the hub computing device 100, the motion sensors 212 and222, the camera 224, the microphone 226, and the entryway sensor 242,may be examples of a controller 73 and sensors 71 and 72, as shown anddescribed in further detail with respect to FIGS. 1-10.

The devices of the security system and smart-home environment of thedisclosed subject matter may be communicatively connected via thenetwork 70, which may be a mesh-type network such as Thread, whichprovides network architecture and/or protocols for devices tocommunicate with one another. Typical home networks may have a singledevice point of communications. Such networks may be prone to failure,such that devices of the network cannot communicate with one anotherwhen the single device point does not operate normally. The mesh-typenetwork of Thread, which may be used in the security system of thedisclosed subject matter, may avoid communication using a single device.That is, in the mesh-type network, such as network 70, there is nosingle point of communication that may fail so as to prohibit devicescoupled to the network from communicating with one another.

The communication and network protocols used by the devicescommunicatively coupled to the network 70 may provide securecommunications, minimize the amount of power used (i.e., be powerefficient), and support a wide variety of devices and/or products in ahome, such as appliances, access control, climate control, energymanagement, lighting, safety, and security. For example, the protocolssupported by the network and the devices connected thereto may have anopen protocol which may carry IPv6 natively.

The Thread network, such as network 70, may be easy to set up and secureto use. The network 70 may use an authentication scheme, AES (AdvancedEncryption Standard) encryption, or the like to reduce and/or minimizesecurity holes that exist in other wireless protocols. The Threadnetwork may be scalable to connect devices (e.g., 2, 5, 10, 20, 50, 100,150, 200, or more devices) into a single network supporting multiplehops (e.g., so as to provide communications between devices when one ormore nodes of the network is not operating normally). The network 70,which may be a Thread network, may provide security at the network andapplication layers. One or more devices communicatively coupled to thenetwork 70 (e.g., controller 73, remote system 74, and the like) maystore product install codes to ensure only authorized devices can jointhe network 70. One or more operations and communications of network 70may use cryptography, such as public-key cryptography.

The devices communicatively coupled to the network 70 of the smart-homeenvironment and/or security system disclosed herein may low powerconsumption and/or reduced power consumption. That is, devicesefficiently communicate to with one another and operate to providefunctionality to the user, where the devices may have reduced batterysize and increased battery lifetimes over conventional devices. Thedevices may include sleep modes to increase battery life and reducepower requirements. For example, communications between devices coupledto the network 70 may use the power-efficient IEEE 802.15.4 MAC/PHYprotocol. In embodiments of the disclosed subject matter, shortmessaging between devices on the network 70 may conserve bandwidth andpower. The routing protocol of the network 70 may reduce networkoverhead and latency. The communication interfaces of the devicescoupled to the smart-home environment may include wirelesssystem-on-chips to support the low-power, secure, stable, and/orscalable communications network 70.

The sensor network shown in FIG. 12 may be an example of a smart-homeenvironment. The depicted smart-home environment may include astructure, a house, office building, garage, mobile home, or the like.The devices of the smart home environment, such as the sensors 71, 72,the controller 73, and the network 70 may be integrated into asmart-home environment that does not include an entire structure, suchas an apartment, condominium, or office space.

The smart home environment can control and/or be coupled to devicesoutside of the structure. For example, one or more of the sensors 71, 72may be located outside the structure, for example, at one or moredistances from the structure (e.g., sensors 71, 72 may be disposedoutside the structure, at points along a land perimeter on which thestructure is located, and the like. One or more of the devices in thesmart home environment need not physically be within the structure. Forexample, the controller 73 which may receive input from the sensors 71,72 may be located outside of the structure.

The structure of the smart-home environment may include a plurality ofrooms, separated at least partly from each other via walls. The wallscan include interior walls or exterior walls. Each room can furtherinclude a floor and a ceiling. Devices of the smart-home environment,such as the sensors 71, 72, may be mounted on, integrated with and/orsupported by a wall, floor, or ceiling of the structure.

The smart-home environment including the sensor network shown in FIG. 12may include a plurality of devices, including intelligent,multi-sensing, network-connected devices that can integrate seamlesslywith each other and/or with a central server or a cloud-computing system(e.g., controller 73 and/or remote system 74) to provide home-securityand smart-home features. The smart-home environment may include one ormore intelligent, multi-sensing, network-connected thermostats (e.g.,“smart thermostats”), one or more intelligent, network-connected,multi-sensing hazard detection units (e.g., “smart hazard detectors”),and one or more multi-sensing, network-connected entryway interfacedevices (e.g., “smart doorbells”). The smart hazard detectors, smartthermostats, and smart doorbells may be the sensors 71, 72 shown in FIG.12.

According to embodiments of the disclosed subject matter, the smartthermostat may detect ambient climate characteristics (e.g., temperatureand/or humidity) and may control an HVAC (heating, ventilating, and airconditioning) system accordingly of the structure. For example, theambient client characteristics may be detected by sensors 71, 72 shownin FIG. 12, and the controller 73 may control the HVAC system (notshown) of the structure.

A smart hazard detector may detect the presence of a hazardous substanceor a substance indicative of a hazardous substance (e.g., smoke, fire,or carbon monoxide). For example, smoke, fire, and/or carbon monoxidemay be detected by sensors 71, 72 shown in FIG. 12, and the controller73 may control an alarm system to provide a visual and/or audible alarmto the user of the smart-home environment.

A smart doorbell may control doorbell functionality, detect a person'sapproach to or departure from a location (e.g., an outer door to thestructure), and announce a person's approach or departure from thestructure via audible and/or visual message that is output by a speakerand/or a display coupled to, for example, the controller 73.

In some embodiments, the smart-home environment of the sensor networkshown in FIG. 12 may include one or more intelligent, multi-sensing,network-connected wall switches (e.g., “smart wall switches”), one ormore intelligent, multi-sensing, network-connected wall plug interfaces(e.g., “smart wall plugs”). The smart wall switches and/or smart wallplugs may be the sensors 71, 72 shown in FIG. 12. The smart wallswitches may detect ambient lighting conditions, and control a powerand/or dim state of one or more lights. For example, the sensors 71, 72,may detect the ambient lighting conditions, and the controller 73 maycontrol the power to one or more lights (not shown) in the smart-homeenvironment. The smart wall switches may also control a power state orspeed of a fan, such as a ceiling fan. For example, sensors 72, 72 maydetect the power and/or speed of a fan, and the controller 73 mayadjusting the power and/or speed of the fan, accordingly. The smart wallplugs may control supply of power to one or more wall plugs (e.g., suchthat power is not supplied to the plug if nobody is detected to bewithin the smart-home environment). For example, one of the smart wallplugs may controls supply of power to a lamp (not shown).

In embodiments of the disclosed subject matter, the smart-homeenvironment may include one or more intelligent, mu(ti-sensing,network-connected entry detectors (e.g., “smart entry detectors”). Thesensors 71, 72 shown in FIG. 12 may be the smart entry detectors. Theillustrated smart entry detectors (e.g., sensors 71, 72) may be disposedat one or more windows, doors, and other entry points of the smart-homeenvironment for detecting when a window, door, or other entry point isopened, broken, breached, and/or compromised. The smart entry detectorsmay generate a corresponding signal to be provided to the controller 73and/or the remote system 74 when a window or door is opened, closed,breached, and/or compromised. In some embodiments of the disclosedsubject matter, the alarm system, which may be included with controller73 and/or coupled to the network 70 may not arm unless all smart entrydetectors (e.g., sensors 71, 72) indicate that all doors, windows,entryways, and the like are closed and/or that all smart entry detectorsare armed.

The smart-home environment of the sensor network shown in FIG. 12 caninclude one or more intelligent, multi-sensing, network-connecteddoorknobs (e.g., “smart doorknob”). For example, the sensors 71, 72 maybe coupled to a doorknob of a door (e.g., doorknobs 122 located onexternal doors of the structure of the smart-home environment). However,it should be appreciated that smart doorknobs can be provided onexternal and/or internal doors of the smart-home environment.

The smart thermostats, the smart hazard detectors, the smart doorbells,the smart wall switches, the smart wall plugs, the smart entrydetectors, the smart doorknobs, the keypads, and other devices of thesmart-home environment e.g., as illustrated as sensors 71, 72 of FIG. 12can be communicatively coupled to each other via the network 70, and tothe controller 73 and/or remote system 74 to provide security, safety,and/or comfort for the smart home environment).

A user can interact with one or more of the network-connected smartdevices (e.g., via the network 70). For example, a user can communicatewith one or more of the network-connected smart devices using a computer(e.g., a desktop computer, laptop computer, tablet, or the like) orother portable electronic device (e.g., a smartphone, a tablet, a keyFOB, and the like). A webpage or application can be configured toreceive communications from the user and control the one or more of thenetwork-connected smart devices based on the communications and/or topresent information about the device's operation to the user. Forexample, the user can view can arm or disarm the security system of thehome.

One or more users can control one or more of the network-connected smartdevices in the smart-home environment using a network-connected computeror portable electronic device. In some examples, some or all of theusers (e.g., individuals who live in the home) can register their mobiledevice and/or key FOBs with the smart-home environment (e.g., with thecontroller 73). Such registration can be made at a central server (e.g.,the controller 73 and/or the remote system 74) to authenticate the userand/or the electronic device as being associated with the smart-homeenvironment, and to provide permission to the user to use the electronicdevice to control the network-connected smart devices and the securitysystem of the smart-home environment. A user can use their registeredelectronic device to remotely control the network-connected smartdevices and security system of the smart-home environment, such as whenthe occupant is at work or on vacation. The user may also use theirregistered electronic device to control the network-connected smartdevices when the user is located inside the smart-home environment.

Alternatively, or in addition to registering electronic devices, thesmart-home environment may make inferences about which individuals livein the home and are therefore users and which electronic devices areassociated with those individuals. As such, the smart-home environment“learns” who is a user (e.g., an authorized user) and permits theelectronic devices associated with those individuals to control thenetwork-connected smart devices of the smart-home environment (e.g.,devices communicatively coupled to the network 70). Various types ofnotices and other information may be provided to users via messages sentto one or more user electronic devices. For example, the messages can besent via email, short message service (SMS), multimedia messagingservice (MMS), unstructured supplementary service data (USSD), as wellas any other type of messaging services and/or communication protocols.

The smart-home environment may include communication with devicesoutside of the smart-home environment but within a proximategeographical range of the home. For example, the smart-home environmentmay include an outdoor lighting system (not shown) that communicatesinformation through the communication network 70 or directly to acentral server or cloud-computing system (e.g., controller 73 and/orremote system 74) regarding detected movement and/or presence of people,animals, and any other objects and receives back commands forcontrolling the lighting accordingly.

The controller 73 and/or remote system 74 can control the outdoorlighting system based on information received from the othernetwork-connected smart devices in the smart-home environment. Forexample, in the event, any of the network-connected smart devices, suchas smart wall plugs located outdoors, detect movement at night time, thecontroller 73 and/or remote system 74 can activate the outdoor lightingsystem and/or other lights in the smart-home environment.

In some configurations, a remote system 74 may aggregate data frommultiple locations, such as multiple buildings, multi-residentbuildings, individual residences within a neighborhood, multipleneighborhoods, and the like. In general, multiple sensor/controllersystems 81, 82 as previously described with respect to FIG. 13 mayprovide information to the remote system 74. The systems 81, 82 mayprovide data directly from one or more sensors as previously described,or the data may be aggregated and/or analyzed by local controllers suchas the controller 73, which then communicates with the remote system 74.The remote system may aggregate and analyze the data from multiplelocations, and may provide aggregate results to each location. Forexample, the remote system 74 may examine larger regions for commonsensor data or trends in sensor data, and provide information on theidentified commonality or environmental data trends to each local system81, 82.

In situations in which the systems discussed here collect personalinformation about users, or may make use of personal information, theusers may be provided with an opportunity to control whether programs orfeatures collect user information (e.g., information about a user'ssocial network, social actions or activities, profession, a user'spreferences, or a user's current location), or to control whether and/orhow to receive content from the content server that may be more relevantto the user. In addition, certain data may be treated in one or moreways before it is stored or used, so that personally identifiableinformation is removed. Thus, the user may have control over howinformation is collected about the user and used by a system asdisclosed herein.

Embodiments of the presently disclosed subject matter may be implementedin and used with a variety of computing devices. FIG. 14 is an examplecomputing device 20 suitable for implementing embodiments of thepresently disclosed subject matter. For example, the device 20 may beused to implement a controller, a device including sensors as disclosedherein, or the like. Alternatively or in addition, the device 20 may be,for example, a desktop or laptop computer, or a mobile computing devicesuch as a smart phone, tablet, or the like. The device 20 may include abus 21 which interconnects major components of the computer 20, such asa central processor 24, a memory 27 such as Random Access Memory (RAM),Read Only Memory (ROM), flash RAM, or the like, a user display 22 suchas a display screen, a user input interface 26, which may include one ormore controllers and associated user input devices such as a keyboard,mouse, touch screen, and the like, a fixed storage 23 such as a harddrive, flash storage, and the like, a removable media component 25operative to control and receive an optical disk, flash drive, and thelike, and a network interface 29 operable to communicate with one ormore remote devices via a suitable network connection.

The bus 21 allows data communication between the central processor 24and one or more memory components 25, 27, which may include RAM, ROM,and other memory, as previously noted. Applications resident with thecomputer 20 are generally stored on and accessed via a computer readablestorage medium.

The fixed storage 23 may be integral with the computer 20 or may beseparate and accessed through other interfaces. The network interface 29may provide a direct connection to a remote server via a wired orwireless connection. The network interface 29 may provide suchconnection using any suitable technique and protocol as will be readilyunderstood by one of skill in the art, including digital cellulartelephone, WiFi, Bluetooth(R), near-field, and the like. For example,the network interface 29 may allow the device to communicate with othercomputers via one or more local, wide-area, or other communicationnetworks, as described in further detail herein.

FIG. 15 shows an example network arrangement according to an embodimentof the disclosed subject matter. One or more devices 10, 11, such aslocal computers, smart phones, tablet computing devices, and the likemay connect to other devices via one or more networks 7. Each device maybe a computing device as previously described. The network may be alocal network, wide-area network, the Internet, or any other suitablecommunication network or networks, and may be implemented on anysuitable platform including wired and/or wireless networks. The devicesmay communicate with one or more remote devices, such as servers 13and/or databases 15. The remote devices may be directly accessible bythe devices 10, 11, or one or more other devices may provideintermediary access such as where a server 13 provides access toresources stored in a database 15. The devices 10, 11 also may accessremote platforms 17 or services provided by remote platforms 17 such ascloud computing arrangements and services. The remote platform 17 mayinclude one or more servers 13 and/or databases 15.

Various embodiments of the presently disclosed subject matter mayinclude or be embodied in the form of computer-implemented processes andapparatuses for practicing those processes. Embodiments also may beembodied in the form of a computer program product having computerprogram code containing instructions embodied in non-transitory and/ortangible media, such as hard drives, USB (universal serial bus) drives,or any other machine readable storage medium, such that when thecomputer program code is loaded into and executed by a computer, thecomputer becomes an apparatus for practicing embodiments of thedisclosed subject matter. When implemented on a general-purposemicroprocessor, the computer program code may configure themicroprocessor to become a special-purpose device, such as by creationof specific logic circuits as specified by the instructions.

Embodiments may be implemented using hardware that may include aprocessor, such as a general purpose microprocessor and/or anApplication Specific Integrated Circuit (ASIC) that embodies all or partof the techniques according to embodiments of the disclosed subjectmatter in hardware and/or firmware. The processor may be coupled tomemory, such as RAM, ROM, flash memory, a hard disk or any other devicecapable of storing electronic information. The memory may storeinstructions adapted to be executed by the processor to perform thetechniques according to embodiments of the disclosed subject matter.

The foregoing description, for purpose of explanation, has beendescribed with reference to specific embodiments. However, theillustrative discussions above are not intended to be exhaustive or tolimit embodiments of the disclosed subject matter to the precise formsdisclosed. Many modifications and variations are possible in view of theabove teachings. The embodiments were chosen and described in order toexplain the principles of embodiments of the disclosed subject matterand their practical applications, to thereby enable others skilled inthe art to utilize those embodiments as well as various embodiments withvarious modifications as may be suited to the particular usecontemplated.

1. A computer-implemented method performed by a data processing apparatus, the method comprising: receiving a signal from a sensor; determining that the sensor is producing anomalous output based on at least one of: checking the signal from the sensor against a signal from a second sensor on the same sensor device as the sensor, checking the signal from the sensor against one or more signals from one or more sensors on one or more additional sensor devices, and checking the signal from the sensor against a slow temporal model for the sensor; and in response to the determination that the sensor is producing anomalous output, at least one of: generating a notification indicating the sensor is producing anomalous output, and degrading a confidence level in the sensor.
 2. The computer-implemented method of claim 1, wherein checking the signal from the sensor against a signal from a second sensor on the same sensor device as the sensor further comprises: receiving the signal from the second sensor on the same sensor device as the sensor; determining that the signal from the sensor is inconsistent with the signal from the second sensor; and generating an indication that the sensor is producing anomalous output.
 3. The computer-implemented method of claim 1, wherein checking the signal from the sensor against one or more signals from one or more sensors on one or more additional sensor devices further comprises: receiving the one or more signals from the one or more sensors on the one or more additional sensor devices; determining that the signal from the sensor is inconsistent with at least one of the one or more signals from the one or more sensors; determining that the one or more signals from the one or more sensors are consistent with each other; and generating an indication that the sensor is producing anomalous output.
 4. The computer-implemented method of claim 1, wherein checking the signal from the sensor against a slow temporal model tier the sensor further comprises: receiving the slow temporal model for the sensor; determining that the signal from the sensor is inconsistent with a signal that the slow temporal model indicates is expected from the sensor in the situation in which the signal from the sensor was generated; and generating an indication that the sensor is producing anomalous output.
 5. The computer-implemented method of claim 4, wherein the slow temporal model for the sensor comprises signals from the sensor over a period of time.
 6. The computer-implemented method of claim 4, wherein the slow temporal model is one of a statistical, probabilistic, or machine learning based model for the signals from the sensor.
 7. The computer-implemented method of claim 4, further comprising determining that the signal from the sensor is inconsistent with a signal that the slow temporal model indicates is expected over a threshold period of time before generating the indication that the sensor is producing anomalous output.
 8. The computer-implemented method of claim 4, further comprising determining that there are no extenuating circumstances causing the signal from the sensor to be inconsistent with the signal that the slow temporal model indicates is expected, before generating the indication that the sensor is producing anomalous output.
 9. The computer-implemented method of claim 8, wherein an extenuating circumstance comprises a security system comprising the sensor being set to an away mode or a vacation mode.
 10. The computer-implemented method of claim 3, wherein the sensor is a motion sensor, and wherein determining that the signal from the sensor is inconsistent with at least one of the one or more signals from the one or more sensors further comprises: determining that the signal from the motion sensor indicates no motion is detected in an environment monitored by the motion sensor; and determining that the at least one of the one or more signals indicates e presence of a moving person within the environment.
 11. The computer-implemented method of claim 10, wherein the environment is a room in a structure.
 12. The computer-implemented method of claim 1, wherein the sensor is one of motion sensor, a passive infrared sensor, a low power motion sensor, a light sensor, a camera, a microphone, and an entryway sensor.
 13. The computer-implemented method of claim 1, wherein the notification is a blocked sensor notification indicating that the sensor is obstructed.
 14. The computer-implemented method of claim 1, wherein the notification is sent to one or more of: a computing device associated with a user of a smart home environment, a display within the smart home environment, and a speaker system within the smart home environment.
 15. A computer-implemented system for blocked sensor detection and notification comprising: a sensor of a smart home environment, the sensor adapted to monitor an aspect of an environment and generate a signal; a storage comprising one or more slow temporal models, at least one of the slow temporal models associated with the sensor; and a hub computing device adapted to receive the signal from the sensor, determine that the sensor is producing anomalous output based on at least one of checking the signal from the sensor against a signal from a second sensor on the same sensor device as the sensor, checking the signal from the sensor against one or more signals from one or more sensors on one or more additional sensor devices, and checking the signal from the sensor against the slow temporal model associated with the sensor, generate a notification indicating the sensor is producing anomalous output, and degrade a confidence level in the sensor.
 16. The computer-implemented system of claim 15, wherein the hub computing device is further adapted to check the signal from the sensor against a signal from a second sensor on the same sensor device as the sensor by receiving the signal from the second sensor on the same sensor device as the sensor, determining that the signal from the sensor is inconsistent with the signal from the second sensor, and generating an indication that the sensor is producing anomalous output.
 17. The computer-implemented system of claim 15, wherein the hub computing device is further adapted to check the signal from the sensor against one or more signals from one or more sensors on one or more additional sensor devices by receiving the one or more signals from the one or more sensors on the one or more additional sensor devices, determining that the signal from the sensor is inconsistent with at least one of the one or more signals from the one or more sensors, determining that the one or more signals from the one or more sensors are consistent with each other, and generating an indication that the sensor is producing anomalous output.
 18. The computer-implemented system of claim 15, wherein the hub computing device is further adapted to check the signal from the sensor against a slow temporal model for the sensor further comprises by receiving the slow temporal model for the sensor, determining that the signal from the sensor is inconsistent with a signal that the slow temporal model indicates is expected from the sensor in the situation in which the signal from the sensor was generated, and generating an indication that the sensor is producing anomalous output.
 19. The computer-implemented system of claim 15, wherein the slow temporal model for the sensor comprises signals from the sensor over a period of time.
 20. The computer-implemented system of claim 15, wherein the slow temporal model is one of a statistical, probabilistic, or machine learning based model for the signals from the sensor.
 21. The computer-implemented system of claim 15, wherein the hub computing device is further adapted to determine that the signal from the sensor is inconsistent with a signal that the slow temporal model indicates is expected over a threshold period of time before generating the indication that the sensor is producing anomalous output.
 22. The computer-implemented system of claim 15, wherein the hub computing device is further adapted to determine that there are no extenuating circumstances causing the signal from the sensor to be inconsistent with the signal that the slow temporal model indicates is expected, before generating the indication that the sensor is producing anomalous output.
 23. The computer-implemented system of claim 22, wherein an extenuating circumstance comprises a security system comprising the sensor being set to an away mode or a vacation mode.
 24. The computer-implemented system of claim 14, wherein the sensor is a motion sensor, and wherein the hub computing device is adapted to determine that the signal from the sensor is inconsistent with at least one of the one or more signals from the one or more sensors further comprises by determining that the signal from the motion sensor indicates no motion is detected in an environment monitored by the motion sensor and determining that the at least one of the one or more signals indicates the presence of a moving person within the environment.
 25. The computer-implemented system of claim 15, wherein the sensor is one of motion sensor, a passive infrared sensor, a low power motion sensor, alight sensor, a camera, a microphone, and an entryway sensor.
 26. The computer-implemented system of claim 15, wherein the notification is a blocked sensor notification indicating that the sensor is obstructed.
 27. The computer-implemented system of claim 15, wherein the hub computing device is further adapted to send the notification to one or more of: a computing device associated with a user of a smart home environment, a display within the smart home environment, and a speaker system within the smart home environment.
 28. A system comprising: one or more computers and one or more storage devices storing instructions which are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: receiving a signal from a sensor; determining that the sensor is producing anomalous output based on at least one of checking the signal from the sensor against a signal from a second sensor on the same sensor device as the sensor, checking the signal from the sensor against one or more signals from one or more sensors on one or more additional sensor devices, and checking the signal from the sensor against a slow temporal model for the sensor; and in response to the determination that the sensor is producing anomalous output, at least one of: generating a notification indicating the sensor is producing anomalous output,and degrading a confidence level in the sensor.
 29. The system of claim 8, wherein the instructions further cause the one or more computers to perform operations comprising: receiving the signal from the second sensor on the same sensor device as the sensor; determining that the signal from the sensor is inconsistent with the signal from the second sensor; and generating an indication that the sensor is producing anomalous output.
 30. The system of claim 28, wherein the instructions further cause be one or more computers to perform operations comprising: receiving the one or more signals from the one or more sensors on the one or more additional sensor devices; determining that the signal from the sensor is inconsistent with at east one of the one or more signals from the one or more sensors; determining that the one or more signals from the one or more sensors are consistent with each other; and generating an indication that the sensor is producing anomalous output.
 31. The system of claim 28, wherein the instructions further cause the one or more computers to perform operations comprising: receiving the slow temporal model for the sensor; determining that the signal from the sensor is inconsistent with a signal that the slow temporal model indicates is expected from the sensor in the situation in which the signal from the sensor was generated; and generating an indication that the sensor is producing anomalous output. 